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  An Energy Efficient Task Offloading for Mobile Cloud Environment  
  Authors : Nancy Arya; Sunita Choudhary; S. Taruna
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In the last few years, the technology of mobile computing has gained tremendous popularity and changed users mind for computing. However, smart phones are constrained computing devices which are facing number of issues related to resources such as memory, storage, computation power and shortened energy. To overcome these constraints, offloading provides natural solution for mobile cloud environment by migrating the intensive problems to cloud servers. However, conventional frameworks of offloading lack in considering the dynamic execution time as well as they have not focused on extra overhead of runtime migration. This paper proposed a new approach for runtime offloading to achieve better performance and energy optimization.


Published In : IJCSN Journal Volume 8, Issue 3

Date of Publication : June 2019

Pages : 305-310

Figures :04

Tables : --


Nancy Arya : Department of Computer Science, Banasthali Vidyapith Rajasthan, India.

Sunita Choudhary : Department of Computer Science, Banasthali Vidyapith Rajasthan, India.

S. Taruna : Department of Computer Science, JK Lakshmipat University Rajasthan, India.


Mobile Computing, Offloading, Runtime Offloading, Energy Optimization

Traditional models have worked to speed up the execution and save the energy for local mobile devices. It is very important for the resource aspects. However, every model has its own benefits and limitations. In the paper, the author has proposed a novel technique for offloading the computational tasks by improving the performance and efficiency of energy. This work used the concept of benchmarking of the computational task before actual execution of the task and on the basis of the estimated value for time and energy, the final decision has taken for offloading. The decision depends on less energy consumption. The results of experiment clearly shows that the proposed work gives better results for energy efficiency and performance. The work has carried out by the execution of complex application of high complexity such as matrix multiplication. For the large matrix size, achieved efficiency around 93.1% for execution time and 96.2% for energy which proves the higher efficiency. The results of the proposed work are then compared to the results of existing framework and shows better efficiency in energy optimization.


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